CN112011616A - Immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time - Google Patents
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Abstract
The invention relates to an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time, belonging to the technical field of biological medicines. The model can be used for evaluating the infiltration degree of immune cells in tumors and improving the prediction capability of the immunotherapy response to liver cancer in clinical practice by detecting the expression levels of specific 22 immune related genes of a hepatocellular carcinoma patient; the microarray chip kit can be used for judging the overall postoperative survival risk of a patient and guiding the formulation of postoperative treatment strategies, and the corresponding microarray chip kit can realize the standardization and the convenience of detection. Meanwhile, the immune gene prognosis model provided by the invention can increase the prediction accuracy and clinical net benefit of a hepatocellular carcinoma TNM staging system on the total survival time of 3 years and 5 years after operation. The invention can be used as a molecular marker for objectively and accurately evaluating the immune state of hepatocellular carcinoma tumor and the risk of poor prognosis, and can realize accurate implementation of hepatocellular carcinoma immunotherapy and accurate prediction of prognosis.
Description
Technical Field
The invention relates to an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time, belonging to the technical field of biological medicines.
Background
Because of the prevalence of chronic liver diseases associated with viral infections and metabolic disorders, the global incidence of hepatocellular carcinoma continues to increase, reaching 2% -3% per year, with the fastest growth among all tumors. According to the statistics of the latest national cancer of the national cancer center in China, the incidence rate of liver cancer is ranked 4 th, which accounts for 5 th of the cause of tumor death, and about 32.6 million people die each year. Frequent recurrence after curative treatment in early stage patients and limited efficacy after systemic treatment in late stage patients lead to poor prognosis of hepatocellular carcinoma, with a 5-year survival rate of only 18%. However, an effective and reliable biomarker is not clinically available at present, and hepatocellular carcinoma patients with poor prognosis are identified in advance to perform treatment intervention in time, so that the survival condition of patients after radical treatment is improved. The alpha fetoprotein level, the tumor size, the existence or nonenveloped of an envelope and the existing tumor staging system have certain reference values for evaluating the prognosis of a patient, but have low overall accuracy and cannot reflect the tumor characteristics of the molecular level.
Immune dysfunction is one of the important biological characteristics in the development process of hepatocellular carcinoma, and immunotherapy represented by immune checkpoint blockade is a very promising strategy in the systemic therapy of hepatocellular carcinoma. However, only a small fraction of hepatocellular carcinoma patients benefit from current immunotherapy, suggesting that the response of different patients to immunotherapy varies greatly. Therefore, it is necessary to search and establish effective prediction indexes of response of hepatocellular carcinoma to current immunotherapy to screen patients who respond to the therapy, realize precise immunotherapy of liver cancer, improve the effective rate of immunotherapy, reduce the exposure of drug toxicity, and improve the survival time of patients in late stage. Studies have suggested that stratification of cancer patients based on the characteristics and quality of tumor immune cell infiltration is a key step in the next phase to predict response to immunotherapy. However, no objective, effective and simple molecular marker capable of predicting the infiltration state of immune cells in hepatocellular carcinoma tumors exists in clinic at present.
Immune-related genes are a series of genes involved in various immune biological processes, including antigen processing presentation, T cell and B cell receptor signaling pathways, and Natural Killer (NK) cytotoxicity, among others. Considering that hepatocellular carcinoma is a malignant tumor characterized by chronic inflammation and immune dysregulation, its disease progression and response to immunotherapy are largely influenced by biological processes regulated by immune-related genes. More and more researches show that a prognosis model constructed based on immune-related genes can accurately evaluate the survival risk of cancer patients, but in hepatocellular carcinoma, a prognosis model reflecting the degree of immune infiltration in tumors and predicting the postoperative survival risk of patients is still to be developed and researched on the basis of immune-related genes.
Disclosure of Invention
The invention aims to solve the technical problems of reflecting the infiltration degree of hepatocellular carcinoma tumor immune cells and predicting the overall postoperative survival risk of a patient by detecting tumor tissues. The invention aims to provide a prognosis model and a microarray chip kit which are based on immune related genes, can reflect the infiltration degree of hepatocellular carcinoma tumor immune cells through tumor tissue detection and predict the overall postoperative survival risk of a patient, and can predict the sensitivity of the patient to immunotherapy according to the tumor infiltration degree so as to guide the immunotherapy; and the patient with poor prognosis can be screened in time to make additional intervention measures, so that the prognosis of the patient is improved.
The second purpose of the invention is to construct a Nomogram prediction model to further fully utilize the immune gene prognosis model and the prognosis complementary evaluation of patient age and tumor TNM stage in the existing prognosis system, so as to accurately predict the possibility of 3-year and 5-year survival time after operation of hepatocellular carcinoma patients and increase the application value of the existing prognosis prediction system.
In order to solve the above problems, the technical scheme adopted by the invention is to provide an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time; the risk score of the prognosis model is obtained by calculating the sum of the expression levels of the 22 immune-related genes after the corresponding coefficient weights, and the tumor immune cell infiltration degree and the patient prognosis adverse risk can be predicted.
Preferably, the 22 immune-related genes are VIPR1, IL20RA, RETN, STC2, PLAUR, NR0B1, PSMD14, GMFB, CAMP, GLP1R, ULBP2, GAL, HDGF, TNF, CCR7, CXCL5, CCR8, BMP6, C5, EPO, GHR and BRD 8.
The invention provides a method for constructing an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time, which comprises the following steps:
step 1: collecting a certain amount of hepatocellular carcinoma tumor samples, extracting tumor RNA, and purifying and sequencing; randomly classifying the patient data into a training set and a verification set;
step 2: identifying immune related genes which are obviously related to prognosis in a training set through single-factor COX regression analysis, and determining genes which are finally brought into a prognosis model and a risk score calculation formula through LASSO regression analysis so as to construct an immune gene prognosis model;
and step 3: calculating the risk score of each sample in the training set according to a calculation formula of the model, dividing the patients into a high risk group and a low risk group based on the median of the sample risk scores, and analyzing the tumor immune infiltration state and the postoperative overall survival difference of the two groups of patients; evaluating the prediction performance of the model through multi-factor COX proportional risk regression analysis and ROC curves in a training set;
and 4, step 4: verifying the prognosis value of the prognosis model based on the immune related gene in a verification set; calculating the risk score of each sample in the verification set, dividing the risk score into a high risk group and a low risk group according to the median in the training set, and comparing whether the overall survival time of two groups of patients has obvious difference after operation.
Preferably, in step 2, the gene set comprising 1534 immune-related genes analyzed by one-way COX regression is downloaded from the Immunology Database and Analysis Portal website (https:// immport.
Preferably, the risk score of each sample in the step 3 is set as the sum of the mRNA expression levels of the immune-related genes in the model weighted by corresponding coefficients; the assessment of tumor immune infiltration status was obtained by single sample gene set enrichment analysis (ssGSEA) analysis of tumor transcriptome data of hepatocellular carcinoma patients in the training set.
The application of the immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time in establishing the Nomogram model for predicting the probability of total survival time of hepatocellular carcinoma for 3 years and 5 years is disclosed.
Preferably, the risk score calculated by the constructed immune gene prognosis model, the age and the tumor TNM stage are used as input factors of a nomogrm model, and the probability value that the postoperative total survival time of the output hepatocellular carcinoma patient reaches 3 years and 5 years is obtained; the calculation method for obtaining the probability value is set as a value on a probability axis corresponding to a position of a sum of point values corresponding to the three input factors on a total point value axis.
The invention also provides application of the immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time in preparing a microarray chip kit for evaluating adverse risk of hepatocellular carcinoma patient prognosis.
Preferably, the microarray chip kit can calculate the risk score of an individual patient by simultaneously detecting the expression levels of 22 corresponding immune-related genes in the tumor tissue RNA, thereby realizing rapid and convenient prognostic risk assessment.
Compared with the prior art, the invention has the following beneficial effects:
(1) by detecting the expression levels of 22 immune-related genes in the tumor tissue after hepatocellular carcinoma surgery and calculating the individualized risk score, the infiltration degree of immune cells in the tumor and the risk of poor prognosis of a patient can be objectively evaluated. The high-grade tumor has relatively low immune cell infiltration, is clinically favorable for evaluating the response of hepatocellular carcinoma patients to immunotherapy and promotes the implementation of accurate treatment of hepatocellular carcinoma.
(2) The prediction complementary action of various prognostic factors is fully utilized by constructing a Nomogram model, and the immune gene prognostic model constructed by the invention is proved to be capable of improving the prognostic evaluation value of the current hepatocellular carcinoma TNM stage. The obtained Nomogram model can quantitatively evaluate the probability of 3-year and 5-year survival time after the operation of the hepatocellular carcinoma patient, has the advantages of accuracy, objectivity and individuation, and can improve the prediction effect of the prognosis risk of the patient and the survival rate after the operation.
Drawings
FIG. 1 is a diagram illustrating the evaluation of prognostic value of a constructed prognostic model of an immune gene according to an embodiment of the present invention. Wherein A is the overall survival time curve for patients in the high and low risk groups in the training set; b is the overall survival time curve for patients in the high and low risk groups in the validation set; the abscissa represents the post-operative survival time (month) and the ordinate represents the overall survival rate; of the two curves in the graph, the upper curve with high overall survival rate represents the low risk group, and the lower curve with low overall survival rate represents the high risk group.
FIG. 2 is a graph showing the prediction performance of the immune gene prognosis model constructed by ROC curve analysis in the training set according to the embodiment of the present invention. The abscissa represents specificity, and the ordinate represents sensitivity; in the figure, the solid line curve represents the prognosis model provided by the present invention, and the dotted line curve represents the TNM staging model.
Fig. 3 shows the difference in the infiltration degree between the T-cell infiltration score (graph a), the cytotoxic cell infiltration score (graph B), the dendritic cell infiltration score (graph C), and the macrophage infiltration score (graph D) in the tumors of patients with higher risk groups, in the case of ssGSEA based on the transcriptome data of hepatocellular carcinoma patients in the training set, according to the embodiment of the present invention. The abscissa of the graph represents the low risk group close to the origin and the ordinate represents the score for the high risk group far from the origin.
Fig. 4 is a Nomogram model diagram which is established based on the constructed immune gene prognosis model risk score and patient age and tumor TNM staging and can predict the 3-year and 5-year survival time probabilities after operation of hepatocellular carcinoma patients in the embodiment of the present invention, and the prediction accuracy and clinical net benefit of hepatocellular carcinoma patients are evaluated by using calibration curve, ROC curve and decision curve analysis.
Wherein A is a Nomogram model plot that can calculate 3-year and 5-year survival probabilities;
b, graph is the calibration curve analysis of the probability value and the true value of 3-year survival time predicted by the Nomogram model; the abscissa in the graph represents the probability of 3-year overall survival predicted by Nomogram, and the ordinate represents the actually observed 3-year overall survival rate.
C, the graph is the calibration curve analysis of the probability value and the true value of the 5-year survival time predicted by the Nomogram model; the abscissa in the graph represents the probability of 5-year overall survival predicted by Nomogram, and the ordinate represents the actually observed 5-year overall survival rate.
Graph D is the prediction performance of the Nomogram model evaluated using ROC curve analysis; in the figure, the abscissa represents specificity and the ordinate represents sensitivity; in the two curves in the figure, the solid line represents the 3-year line, and the dotted line represents the 5-year line.
Figure E is a decision curve analysis of the nomogrm model and the constructed immune gene prognosis model to assess both clinical net gains. In the graph, the abscissa represents survival threshold probability and the ordinate represents clinical net benefit; in the figure, the solid line in the three curves represents the 3-year line without decision by the conventional model, the "+" line represents the 3-year line with risk evaluation by the immune gene prognosis model, and the dashed line represents the 3-year line with risk evaluation by the Nomogram model.
Graph F is a decision curve analysis of the nomogrm model and the constructed immune gene prognosis model to assess both clinical net gains. In the graph, the abscissa represents survival threshold probability and the ordinate represents clinical net benefit; in the three curves in the figure, the solid line represents the conventional 5-year line without model decision making, the "+" line represents the 5-year line with risk assessment by the immune gene prognosis model, and the dashed line represents the 5-year line with risk assessment by the Nomogram model.
Detailed Description
In order to make the invention more comprehensible, preferred embodiments are described in detail below with reference to the accompanying drawings:
the invention aims to provide a prognosis model and a microarray chip kit which are based on immune related genes, can reflect the infiltration degree of hepatocellular carcinoma tumor immune cells through tumor tissue detection and predict the overall postoperative survival risk of a patient, and can predict the sensitivity of the patient to immunotherapy according to the tumor infiltration degree so as to guide the immunotherapy; the patient with poor prognosis can be screened in time to make additional intervention measures, and the prognosis of the patient is improved.
The second purpose of the invention is to construct a Nomogram prediction model so as to further fully utilize the immune gene prognosis model and the prognosis complementary value of the patient age and tumor TNM stage, accurately predict the possibility of 3-year and 5-year survival time after operation of hepatocellular carcinoma patients, and increase the application value of the existing prognosis prediction system.
In order to construct an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time, the technical scheme adopted by the invention is as follows:
1. extracting RNA components of the collected hepatocellular carcinoma tumor tissues, purifying and sequencing the RNA components, and randomly dividing patients into a training set and a verification set; the set of immune-related genes was downloaded from the Immunology Database and Analysis Portal website.
2. And performing single-factor COX regression analysis based on the data of the postoperative total survival time of the patients in the training set to identify immune-related genes statistically relevant to prognosis, and further determining the most valuable genes and corresponding weights thereof by adopting LASSO regression analysis to construct an immune gene prognosis model. The risk score is the sum of the mRNA expression levels of the immune-related genes included in the model after weighting by corresponding coefficients, the median is used as a threshold value to divide the patients into a high risk group and a low risk group, and the prognosis value of the model is evaluated through survival analysis, ROC curve and multi-factor COX proportional risk regression analysis. The difference in the degree of immune cell infiltration of the tumors of the two groups of patients was compared by single sample gene set enrichment analysis (ssGSEA).
3. The capability of the immune gene prognosis model for predicting the postoperative survival risk of hepatocellular carcinoma is verified in a verification set: calculating the risk score of each tumor sample in the verification set, dividing the patients into a high risk group and a low risk group by taking the median in the training set as a boundary value, and comparing the difference of the overall postoperative survival time of the two groups of patients.
Through the steps, the high-risk hepatocellular carcinoma patient identified based on the constructed immune gene prognosis model is found to have poorer overall survival, lower infiltration of T cells, cytotoxic cells and dendritic cells in tumors and higher infiltration of macrophages.
To achieve the second objective, a Nomogram prediction model is constructed, and the solution adopted by the invention is as follows: independent risk factors, namely model risk score, patient age and tumor TNM stage, of the hepatocellular carcinoma patient total survival time obtained by multi-factor COX regression analysis are used as input factors of a Nomogram model, and the probability that the hepatocellular carcinoma patient survival reaches 3 years and 5 years after surgery is calculated as follows: (the values of the risk score of the immune gene prognosis model on the axis of the dot + the value of the patient's age on the axis of the dot + the values of the stages of the tumor TNM on the axis of the dot) vertically correspond to the values on the corresponding probability axis. The method for constructing and evaluating the Nomogram model comprises the following steps: the Nomogram model was constructed by calculating the partial regression coefficients for each individual risk factor by multifactor COX regression analysis. And analyzing and evaluating the prediction accuracy and the clinical net benefit brought by the prediction effect of the Nomogram model by adopting a calibration curve, an ROC curve and a decision curve.
Examples
1. Study subjects:
the study subjects of this embodiment are 471 postoperative tumor tissues of hepatocellular carcinoma patients, and the inclusion and exclusion criteria are:
(1) pathological diagnosis is hepatocellular carcinoma;
(2) cancer treatment has not been performed before;
(3) no other tumor history;
(4) perfect clinical pathological data and follow-up information.
2. The research method comprises the following steps:
(1) 471 postoperative tumor tissue specimens of hepatocellular carcinoma patients are randomly divided into a training set and a verification set, wherein the number of samples in the training set is 250, and the number of samples in the verification set is 221. Tumor tissue RNA was extracted using TRIzol reagent, RNA purity was measured using a nanophotometer, RNA integrity was measured using an Agilent 2100bioanalyzer, followed by library construction and transcriptome Illumina sequencing.
(2) A gene set containing 1534 immune-related genes was downloaded from the Immunology Database and Analysis Portal website. In the training set, the immune-related genes which are significantly related to prognosis are identified by single-factor COX regression analysis in combination with the overall survival time data of the patient after surgery. Then, LASSO regression analysis is carried out by using a glmnet package of R language software to screen the significant genes, and a tenfold cross validation is utilized to determine an optimal punishment parameter lambda so as to determine the final most valuable immune related genes and corresponding coefficients thereof, thereby constructing an immune gene prognosis model.
(3) And calculating the risk score of each sample in the training set and the verification set according to a calculation formula of the model, wherein the calculation method is the sum of the expression levels of the immune related genes contained in the model after weighting by corresponding coefficients. And respectively dividing the patients in the two queues into a high-risk group and a low-risk group according to the scoring median of the training set samples, and performing Kaplan-Meier survival analysis and Log-rank test on the postoperative total survival time of the two groups of patients. And (3) evaluating the prediction performance of the model in a training set through multi-factor COX proportional risk regression analysis and ROC curves. The infiltration of 4 immune cell types (T cells, cytotoxic cells, dendritic cells and macrophages) in each sample was calculated using the ssGSEA method in the gsva package of the R language software and sequencing data. The difference between the degree of tumor immune cell infiltration in patients with higher low risk group.
(4) Independent risk factors for overall survival time after hepatocellular carcinoma surgery identified using rms package in language R software versus multifactorial COX proportional hazards regression analysis can be visualized as the Nomogram model. The accuracy, predictive efficacy and clinical net benefit of the Nomogram model were evaluated using calibration, ROC and decision curve analysis.
3. The experimental results are as follows:
(1) in the training set, 78 genes that were significantly correlated with the overall survival time of hepatocellular carcinoma patients were first identified from 1534 immune-related genes by univariate Cox regression analysis. Subsequently, 22 most valuable genes were finally selected by LASSO Cox regression analysis to construct an immune gene prognosis model. Specific genes and their corresponding coefficients are shown in table 1. And the risk score of each sample is the sum of the immune related genes weighted by corresponding coefficients, and the scores of the samples in the training set and the verification set are calculated. Patients were divided into high risk groups and low risk groups with the number of digits in the training set as a cutoff. Survival analysis showed that the overall survival time after surgery was significantly reduced in the high risk group patients as shown in panels a and B of fig. 1.
Table 1 molecular label comprising 22 immune related genes and coefficients thereof
(2) The results of the multifactorial COX proportional hazards regression analysis showed that the risk score is an independent risk factor for the overall post-operative survival of hepatocellular carcinoma patients (risk ratio 5.51, 95% confidence interval 3.40-8.96, P < 0.0001), as shown in table 2. Meanwhile, ROC curve analysis indicates that the prediction efficiency of the constructed immune gene prognosis model is higher than that of the existing hepatocellular carcinoma prognosis prediction system (the area under the curve is 0.67[ the 95% confidence interval is 0.62-0.72]) as shown in fig. 2. In conclusion, the prognosis model constructed by the invention can more accurately judge the poor prognosis risk of the hepatocellular carcinoma patient, and has a larger clinical application value.
TABLE 2 one-and multifactorial COX proportional Risk regression analysis of postoperative Overall survival time for hepatocellular carcinoma patients
(3) ssGSEA analysis of transcriptome data revealed that tumors in high risk group patients tended to be in a state of relatively low infiltration of T cells, cytotoxic cells and dendritic cells, while macrophage infiltration was relatively high, as shown in figure 3. The risk score of the prognosis model constructed by the invention is proved to reflect the characteristics of tumor immune cell infiltration to a certain extent.
(4) The prognostic model risk score, patient age, and tumor TNM staging were integrated to construct a nomogrm model that predicts the probability of 3-year and 5-year survival times post-surgery, as shown in fig. 4, panel a. The results of the calibration curve analysis show that the 3-year and 5-year survival time probabilities predicted by the Nomogram model are highly consistent with the actual observed true values, which proves that the prediction accuracy is high, as shown in B, C in fig. 4. The ROC curve indicates that the Nomogram model has a high predictive performance, as shown in graph D of fig. 4. In addition, the results of the decision curve analysis are shown in fig. 4, panels E and F, and when an immune-related prognostic model is combined with age, TNM staging for 3-year and 5-year survival prediction after surgery, the patient receives more clinical net benefit from the prediction with the same risk threshold probability.
While the invention has been described with respect to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Those skilled in the art can make various changes, modifications and equivalent arrangements, which are equivalent to the embodiments of the present invention, without departing from the spirit and scope of the present invention, and which may be made by utilizing the techniques disclosed above; meanwhile, any changes, modifications and variations of the above-described embodiments, which are equivalent to those of the technical spirit of the present invention, are within the scope of the technical solution of the present invention.
Claims (9)
1. An immune gene prognostic model for predicting hepatocellular carcinoma tumor immunoinfiltration and postoperative survival time; the method is characterized in that: the risk score of the prognosis model is obtained by calculating the sum of the expression levels of the 22 immune-related genes after the corresponding coefficient weights, and the tumor immune cell infiltration degree and the patient prognosis adverse risk can be predicted.
2. The prognostic immune gene model for predicting hepatocellular carcinoma tumor infiltration and post-operative survival time of claim 1, wherein: the 22 immune-related genes are VIPR1, IL20RA, RETN, STC2, PLAUR, NR0B1, PSMD14, GMFB, CAMP, GLP1R, ULBP2, GAL, HDGF, TNF, CCR7, CXCL5, CCR8, BMP6, C5, EPO, GHR and BRD 8.
3. A method for constructing an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time is characterized in that: the method comprises the following steps:
step 1: collecting a certain amount of hepatocellular carcinoma tumor samples, extracting tumor RNA, and purifying and sequencing; randomly classifying the patient data into a training set and a verification set;
step 2: identifying immune related genes which are obviously related to prognosis in a training set through single-factor COX regression analysis, and determining genes which are finally brought into a prognosis model and a risk score calculation formula through LASSO regression analysis so as to construct an immune gene prognosis model;
and step 3: calculating the risk score of each sample in the training set according to a calculation formula of the model, dividing the patients into a high risk group and a low risk group based on the median of the sample risk scores, and analyzing the tumor immune infiltration state and the postoperative overall survival difference of the two groups of patients; evaluating the prediction performance of the model through multi-factor COX proportional risk regression analysis and ROC curves in a training set;
and 4, step 4: verifying the prognosis value of the prognosis model based on the immune related gene in a verification set; calculating the risk score of each sample in the verification set, dividing the risk score into a high risk group and a low risk group according to the median in the training set, and comparing whether the overall survival time of two groups of patients after operation has obvious difference.
4. The method of claim 3 for constructing an immune genetic prognostic model for predicting hepatocellular carcinoma tumor immunoinfiltration and post-operative survival time, wherein: in step 2, a gene set containing 1534 immune-related genes analyzed by one-way COX regression was downloaded from the Immunology Database and Analysis Portal website (https:// immport.
5. The method of claim 3 for constructing an immune genetic prognostic model for predicting hepatocellular carcinoma tumor immunoinfiltration and post-operative survival time, wherein: setting the risk score of each sample in the step 3 as the sum of the mRNA expression level of the immune related genes in the model after weighting by corresponding coefficients; the assessment of tumor immune infiltration status was obtained by single sample gene set enrichment analysis (ssGSEA) analysis of tumor transcriptome data of hepatocellular carcinoma patients in the training set.
6. An application of immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time in establishing a nomogrm model for predicting probability of total survival time of hepatocellular carcinoma for 3 years and 5 years.
7. The use of the immunogenetic prognostic model for predicting hepatocellular carcinoma tumor infiltration and postoperative survival according to claim 6 in the construction of a Nomogram model for predicting probability of total 3 and 5 year survival of hepatocellular carcinoma, characterized in that: the risk score calculated by the constructed immune gene prognosis model, the age and the tumor TNM stage are used as input factors of a Nomogram model, and the probability values that the postoperative total survival time of the hepatocellular carcinoma patient reaches 3 years and 5 years are output; the calculation method for obtaining the probability value is set as a value on a probability axis corresponding to a position of a sum of point values corresponding to the three input factors on a total point value axis.
8. An application of immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time in preparing microarray chip kit for evaluating adverse risk of hepatocellular carcinoma patient prognosis.
9. The use of the immune gene prognostic model for predicting hepatocellular carcinoma tumor immunoinfiltration and postoperative survival time according to claim 8 in the preparation of microarray chip kit for evaluating the risk of poor prognosis of hepatocellular carcinoma patients, characterized in that: the microarray chip kit can calculate the risk score of an individual patient by simultaneously detecting the expression levels of 22 corresponding immune related genes in the RNA of a tumor tissue, thereby realizing rapid and convenient prognosis risk assessment.
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